Generally, image forgery can be broadly categorized into: splicing [1]}, [2]}, copy-move [3]}, [4]}, [5]}, removal [6]}, enhancement [7]}, [8]}, etc. First, in image splicing, content is copied and pasted from other source images, as opposed to copy-move forgery, where the content is obtained from the same image. On the other hand, removal or inpainting techniques remove a selected region from the image and fills the space with new pixel values estimated from background [9]}. Image enhancement exploits a wide collection of local manipulations, such as sharpening, brightness adjustment, etc. Each of the broader categories can be further divided into more fine-grained forgery types. For example, Gaussian blurring or JPEG compression may be applied to the tampered region before committing splicing or copy-move forgery. Recently, more general-purpose image forgery localization methods have been proposed, which can detect or localize more than one forgery type, such as RGB-N Net [10]}, Manipulation Tracing Network (ManTraNet) [9]}, Spatial Pyramid Attention Network (SPAN) [12]}, etc.
